Nomogram for 5-year relapse-free survival of a patient with advanced gastric cancer after surgery

Effects of Clavien-Dindo Classification on Long-Term Survival of Patients With Advanced Gastric Cancer After Radical Resection: A Propensity Score-matched Study

BACKGROUND

The impact of postoperative complications (POCs) classified by the Clavien-Dindo (C-D) system on long-term survival after radical resection in patients with advanced gastric cancer (AGC) is not yet clear.

METHODS

This study analyzed 531 patients with AGC who underwent radical resection in an institution between January 2015 and December 2017. Patients were divided into 2 groups according to the occurrence of POCs and recorded according to C-D classifications. The long-term survival outcomes of the entire cohort after propensity score matching (PSM) were compared.

RESULTS

After PSM, there was no significant difference in baseline data between the complications (C) group (n = 92) and the non-complications (NC) group (n = 92). Survival analysis showed that the 5-year overall survival (OS) and relapse-free survival (RFS) were lower in the C group (48.9% vs 62.0%, P = .040; 38.5% vs 54.9%, P = .005; respectively). Subgroup analysis showed that severe complications (C-D grade > II) were associated with a decrease in 5-year OS and RFS compared with the matched NC group (40.0% vs 62.0%, P = .008; 29.4% vs 54.9%, P = .001; respectively). Multivariate analysis confirmed adjuvant chemotherapy, tumor size, and complications were independent risk factors for poor survival outcomes. Further multivariate analysis showed that older age, combined excision, and comorbidities were independent risk factors for POCs.

CONCLUSIONS

Severe complications reduced the survival outcome of patients. More attention should be paid to perioperative management of patients with high risk factors for complications.

Development and external validation of a nomogram predicting overall survival for Gastric adenocarcinoma patients with radical gastrectomy

PURPOSE

The aim of this study was to develop and externally validate a nomogram to accurately predict the overall survival (OS) of patients with gastric adenocarcinoma who underwent radical gastrectomy.

MATERIALS AND METHODS

A total of 3492 patients with gastric adenocarcinoma who underwent radical gastrectomy from 2012 to 2017 were included as the training cohort. Survival analysis was performed via Kaplan Meier method and log-rank test. Independent postoperative prognostic factors in patients with gastric adenocarcinoma were analyzed using univariate and multifactorial COX analysis methods. The prognosis nomogram was established in the training cohort and verified externally in the Surveillance, Epidemiology and End Results (SEER) database.

RESULTS

According to the univariate and multifactorial COX analyses, metastatic lymph node ratio (MLNR) and five other independent prognostic factors (age at surgery, type of gastrectomy, tumor size, T stage, and pathological grade) were included in the prognostic nomogram. The nomogram had better prognostic predictive ability than the American Joint Committee on Cancer (AJCC) TNM staging in both the training (C-index: 0.736 VS. 0.668) and external validation cohort (C-index: 0.712 VS. 0.627). The calibration plots showed that the predicted survival rate was in good agreement with the actual survival rate. And the decision curve analysis (DCA) curves revealed that nomogram showed stronger ability in predicting 1-year, 3-year, and 5-year OS.

CONCLUSION

This study estimated the excellent prognostic predictive power and clinical application potential of the MLNR-based nomogram, which may be used to facilitate postoperative clinical treatment decisions and potentially improve patient survival outcomes.

A web-based prediction model for long-term cancer-specific survival of middle-aged patients with early-stage gastric cancer: a multi-institutional retrospective study

BACKGROUND

This study constructed and validated a prognostic model to evaluate long-term cancer-specific survival (CSS) in middle-aged patients with early gastric cancer (EGC).

METHODS

We extracted clinicopathological data from relevant patients between 2004 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided the patients into a training group (N = 688) and a validation group (N = 292). In addition, 102 Chinese patients were enrolled for external validation. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors, and a nomogram was constructed to predict CSS. We used the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the predictive performance of the model.

RESULTS

Univariate and multivariate COX regression analyses showed that tumor location, differentiation grade, N stage, chemotherapy, and number of regional nodes examined were independent risk factors for prognosis, and these factors were used to construct the nomogram. The C-index of the model in the training cohort, internal validation cohort, and external validation cohort was 0.749 (95% CI 0.699-0.798), 0.744 (95% CI 0.671-0.818), and 0.807 (95% CI 0.721-0.893), respectively. The calibration curve showed that the model had an excellent fit. The DCA curve showed that the model had good predictive performance and practical clinical value.

CONCLUSION

This study developed and validated a new nomogram to predict CSS in middle-aged patients with EGC. The prediction model has unique and practical value and can help doctors carry out individualized treatment and judge prognosis.

Prognostic factors affecting the ruptured intracranial aneurysms: A 9-year multicenter study in Fujian, China

BACKGROUND

A multicenter retrospective study was conducted to explore the factors affecting short-term prognosis and long-term outcomes of intracranial aneurysms (IA) rupture. Further, the prognosis prediction model was constructed based on survival analysis, contributing to the development of prevention strategies for aneurysmal subarachnoid hemorrhage.

METHODS

Data of 1280 patients with IA rupture were gathered between 2014 and 2022 in Fujian, China. Logistic regression was implemented to study the short-term prognostic factors of IA rupture. Survival analysis of 911 patients among them was performed to explore the long-term outcome status by Cox risk assessment. Nomogram prognosis models were constructed using R software.

RESULTS

The findings displayed that blood type O (OR = 1.79; P = 0.019), high systolic pressure (OR = 1.01; P < 0.001), Glasgow Coma score (GCS) 9-12 (OR = 2.73; P = 0.022), GCS < 9 (OR = 3.222; P = 0.006), diabetes (OR = 2.044; P = 0.040), and high white blood cell count (OR = 1.059, P = 0.040) were core influencing factors for poor short-term prognosis. Survival analysis revealed that age > 60 years (HR = 2.87; P = 0.001), hypertension (HR = 1.95; P = 0.001), conservative (HR = 6.89; P < 0.001) and endovascular treatment (HR = 2.20; P = 0.001), multiple ruptured IAs (HR = 2.37; P = 0.01), Fisher 3 (HR = 1.68; P = 0.09), Fisher 4 (HR = 2.75; P = 0.001), and Hunt-Hess 3 (HR = 0.55; P = 0.05) were the major risk factors for terrible long-term outcomes.

CONCLUSIONS

People over 60 years with characteristics of type O blood, high systolic pressure, diabetes, high white blood cell count, and onset GCS < 12 will have more complications and a worse short-term prognosis. Those aged > 60 years with hypertension, conservative and endovascular treatment, multiple ruptured IAs, Fisher ≥ 3 and Hunt-Hess 3 have a greater risk of poor long-term prognosis.

Early-onset locally advanced rectal cancer characteristics, a practical nomogram and risk stratification system: a population-based study

Background

The purpose of this study is to construct a novel and practical nomogram and risk stratification system to accurately predict cancer-specific survival (CSS) of early-onset locally advanced rectal cancer (EO-LARC) patients.

Methods

A total of 2440 patients diagnosed with EO-LARC between 2010 and 2019 were screened from the Surveillance, Epidemiology, and End Results (SEER) database. The pool of potentially eligible patients was randomly divided into two groups: a training cohort (N=1708) and a validation cohort (N=732). The nomogram was developed and calibrated using various methods, including the coherence index (C-index), receiver operating characteristic curve (ROC), calibration curves, and decision curves (DCA). A new risk classification system was established based on the nomogram. To compare the performance of this nomogram to that of the American Joint Committee on Cancer (AJCC) staging system, DCA, net reclassification index (NRI), and integrated discrimination improvement (IDI) were employed.

Result

Seven variables were included in the model. The area under the ROC curve (AUC) for the training cohort was 0.766, 0.736, and 0.731 at 3, 6, and 9 years, respectively. Calibration plots displayed good consistency between actual observations and the nomogram's predictions. The DCA curve further demonstrated the validity of the nomination form in clinical practice. Based on the scores of the nomogram, all patients were divided into a low-risk group, a middle-risk group, and a high-risk group. NRI for the 3-, 6-, and 9-year CSS(training cohort: 0.48, 0.45, 0.52; validation cohort: 0.42, 0.37, 0.37), IDI for the 3-, 6-, and 9-year CSS (training cohort: 0.09, 0.10, 0.11; validation cohort: 0.07, 0.08, 0.08). The Kaplan-Meier curve revealed that the new risk classification system possesses a more extraordinary ability to identify patients in different risk groups than the AJCC staging.

Conclusion

A practical prognostic nomogram and novel risk classification system have been developed to efficiently predict the prognosis of EO-LARC. These tools can serve as a guide to individualize patient treatment and improve clinical decision-making.

Nomogram to Predict Recurrence and Guide a Pragmatic Surveillance Strategy After Resection of Hepatoid Adenocarcinoma of the Stomach: A Retrospective Multicenter Study

BACKGROUND

An accurate recurrence risk assessment system and surveillance strategy for hepatoid adenocarcinoma of the stomach (HAS) remain poorly defined. This study aimed to develop a nomogram to predict postoperative recurrence of HAS and guide individually tailored surveillance strategies.

METHODS

The study enrolled all patients with primary HAS who had undergone curative-intent resection at 14 institutions from 2004 to 2019. Clinicopathologic variables with statistical significance in the multivariate Cox regression were incorporated into a nomogram to build a recurrence predictive model.

RESULTS

The nomogram of recurrence-free survival (RFS) based on independent prognostic factors, including age, preoperative carcinoembryonic antigen, number of examined lymph nodes, perineural invasion, and lymph node ratio, achieved a C-index of 0.723 (95% confidence interval [CI], 0.674-0.772) in the whole cohort, which was significantly higher than those of the eighth American Joint Committed on Cancer (AJCC) staging system (C-index, 0.629; 95% CI, 0.573-0.685; P < 0.001). The nomogram accurately stratified patients into low-, middle-, and high-risk groups of postoperative recurrence. The postoperative recurrence risk rates for patients in the middle- and high-risk groups were respectively 3 and 10 times higher than for the low-risk group. The patients in the middle- and high-risk groups showed more recurrence and metastasis, particularly multiple site metastasis, within 36 months after the operation than those in the low-risk group (low, 2.2%; middle, 8.6%; high, 24.0%; P = 0.003).

CONCLUSIONS

The nomogram achieved good prediction of postoperative recurrence for the patients with HAS after radical resection. For the middle- and high-risk patients, more active surveillance and targeted examination methods should be adopted within 36 months after the operation, particularly for liver and multiple metastases.

Construction and validation of a prognostic prediction model for gastric cancer using a series of genes related to lactate metabolism

Background

Gastric cancer (GC) is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. The commonly used tumor-node-metastasis (TNM) staging and some common biomarkers have a certain value in predicting the prognosis of GC patients, but they gradually fail to meet the clinical demands. Therefore, we aim to construct a prognostic prediction model for GC patients.

Methods

A total of 350 cases were included in the STAD (Stomach adenocarcinoma) entire cohort of TCGA (The Cancer Genome Atlas), including the STAD training cohort of TCGA (n = 176) and the STAD testing cohort of TCGA (n = 174). GSE15459 (n = 191), and GSE62254 (n = 300) were for external validation.

Results

Through differential expression analysis and univariate Cox regression analysis in the STAD training cohort of TCGA, we screened out five genes among 600 genes related to lactate metabolism for the construction of our prognostic prediction model. The internal and external validations showed the same result, that is, patients with higher risk score were associated with poor prognosis (all p < 0.05), and our model works well without regard of patients' age, gender, tumor grade, clinical stage or TNM stage, which supports the availability, validity and stability of our model. Gene function analysis, tumor-infiltrating immune cells analysis, tumor microenvironment analysis and clinical treatment exploration were performed to improve the practicability of the model, and hope to provide a new basis for more in-depth study of the molecular mechanism for GC and for clinicians to formulate more reasonable and individualized treatment plans.

Conclusions

We screened out and used five genes related to lactate metabolism to develop a prognostic prediction model for GC patients. The prediction performance of the model is confirmed by a series of bioinformatics and statistical analysis.

Family history of malignant tumor is a predictor of gastric cancer prognosis: Incorporation into a nomogram

The purpose of this study was to investigate the impact of a family history of malignant tumor on the prognosis of patients with gastric cancer and develop a nomogram that incorporates a family history of malignant tumor to predict overall survival (OS) in patients with gastric cancer to aid clinicians and patients in decision making. Four hundred eighty-eight patients with gastric cancer undergoing radical gastrectomy in our center were included and randomly split into a training set (n = 350) and a validation set (n = 138) at a ratio of 7:3. Cox univariate regression analysis was used to evaluate the influence of clinicopathological characteristics and family history of malignant tumors on their prognosis, and variables were screened by multivariate Cox regression analysis and consensus on clinical evidence. A nomogram was constructed for OS based on the filtered variables, and the C-index, receiver operating characteristic curve (ROC curve), and calibration curve were used to validate the nomogram and decision curve analysis curve (DCA curve) was used for clinical practicality assessment. Six variables related to OS, including the pathological differentiation degree, Lauren type, infiltration depth, lymph node metastasis, tumor deposit, and family history of malignant tumor, were screened to construct a nomogram. The nomogram developed in this study performed well in the training set and the validation set, with C-index of 0.776 and 0.757, and the area under the ROC curve(AUC) for predicting 1-, 3-, and 5-year survival rates are 0.838, 0.850, 0.820 and 0.754, 0.789, 0.808, respectively. The calibration curve shows that the estimated death risk of the nomogram in the 2 data sets is very close to the actual death risk. The net benefits of nomogram-guided prediction of patient survival at 1-, 3-, and 5 years were demonstrated by the DCA curves, which showed high clinical practicability. Family history of malignant tumors is an independent risk factor affecting the prognosis of patients with gastric cancer. The nomogram developed in this research can be used as an important tool to predict the prognosis of gastric cancer patients with family history data.

A nomogram to predict survival probability of gastric cancer patients undergoing radical surgery and adjuvant chemotherapy

Gastric cancer (GC) is the third-leading cause of cancer mortality worldwide. The aim of this study was to develop a nomogram that estimates 1-year, 3-year, and 5-year survival probability of GC patients after D2 gastrectomy combined with adjuvant chemotherapy. The results showed that median age is 58 (range: 18-85) years in the training cohort and 59 (range: 32-85) years in the validation cohort. On multivariate analysis, four factors were found to be significantly associated with worse overall survival (OS): late TNM stage, positive resection margin, preoperative carcinoembryonic antigen (CEA) level, and single chemotherapy regimens compared with multiple chemotherapy regimens. All of these findings were validated in the validation cohort. Furthermore, the four factors were included in the final nomogram for the prediction of 1-year, 3-year, and 5-year survival probability, with accurate calibration and reasonable discrimination (C-index = 0.676 for training cohort, and C-index = 0.664 for validation cohort). The AUC values analyzed by the ROC analysis demonstrated a good predictive accuracy of the nomogram for OS (1-year, 3-year, and 5-year OS were 94.43%, 77.42%, and 73.03% in the training cohort, respectively; 96.95%, 81.54%, and 73.41% in the validation cohort, respectively). In conclusion, the proposed nomogram may be used to objectively and accurately predict survival probability of GC patients in a multi-institutional clinical setting.

CircNR3C1 Alleviates Gastric Cancer Development by Inactivating AKT/mTOR

Differential level and regulatory effect of circNR3C1 in gastric cancer (GC) were determined. The differential levels of circNR3C1 in clinical samples of GC were determined. The association of circNR3C1 level with pathological indicators of GC was analyzed. After intervening circNR3C1 levels in gastric cancer cells, proliferative and migratory changes were investigated. Furthermore, we measured AKT and mTOR protein levels in GC cells intervened by circNR3C1. Finally, the role of AKT/mTOR in GC cell phenotypes regulated by circNR3C1 was explored. circNR3C1 was markedly lowly expressed in GC cells and tissues. A low level of circNR3C1 predicted high incidences of lymphatic or distant metastasis of GC. Knockdown of circNR3C1 enhanced proliferation and migration abilities in BGC-823 cells, whereas overexpression of circNR3C1 yielded the opposite results in AGS cells. circNR3C1 downregulated mTOR and AKT in GC cells. In addition, induction of the AKT activator could reverse the attenuated proliferative and migratory potentials in GC cells overexpressing circNR3C1. On the contrary, induction of the AKT inhibitor reversed the stimulated malignant phenotypes of GC with circNR3C1 knockdown. circNR3C1 inhibits GC to proliferate and migrate by inactivating the AKT/mTOR signaling. It is also closely linked to GC metastasis.

Establishment and Validation of a Nomogram Based on Negative Lymph Nodes to Predict Survival in Postoperative Patients with non-Small Cell Lung Cancer

Background: The importance of the negative lymph node (NLN) count has recently attracted attention. This study aimed to determine the prognostic value of NLN count in patients with non-small cell lung cancer (NSCLC) after radical surgery by constructing NLN-based prognostic models. Methods: This study included 33 756 patients pooled from the case listing session of the US Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and 545 patients collected from The First Affiliated Hospital of Shandong First Medical University between 2012 and 2016. X-tile software was used to calculate the optimal cutoff value for the NLN count. The associated clinical factors were determined using univariate and multivariate Cox analyses. Nomograms were developed using the SEER database and validated using hospital data. Results: The training cohort was divided into high and low NLN count subgroups based on the cancer-specific survival (CSS) and overall survival (OS), respectively. Multivariate analysis showed that NLN count was an independent prognostic factor, and the high NLN count subgroup had better CSS and OS than those of the low NLN count subgroup (HR = 0.632, 95% CI 0.551-0.724, P < .001 for CSS and HR = 0.641, 95% CI 0.571-0.720, P < .001 for OS). Nomograms were established, exhibiting good discrimination ability with a C-index of 0.789 (95% CI 0.778 −0.798) for CSS and 0.704 (95% CI, 0.694 −0.714) for OS. The calibration plots of the validation cohorts showed optimal agreement with the training cohort, with a C-index of 0.681 (95% CI 0.646 −0.716) for CSS and 0.645 (95% CI 0.614 −0.676) for OS. Conclusions: NLN count is a strong prognostic factor for OS and CSS in NSCLC patients and the prognostic model provides a useful risk stratification for NSCLC patients when applied to clinical practice.

Development and external validation of a prognostic nomogram for patients with gastric cancer after radical gastrectomy

Background

Gastric cancer (GC) is one of the most malignant diseases and threatens the health of individuals across the globe. Hitherto, the identification of prognosis risk stratification on GC has mainly depended on the TNM staging, but owing to its inaccuracy and incompleteness, the prognostic value it offers remains controversial in the current clinical setting. Thus, an effective prognostic model for GC after radical gastrectomy is still needed.

Methods

Patients with pathologically confirmed GC who underwent radical gastrectomy from 2 different centers were retrospectively enrolled into a training and the validation cohort, respectively. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select variables among multiple factors, including clinical characteristics, pathological parameters, and surgery- and treatment-related indicators. The multivariate Cox regression method was used to establish the model to predict 1-, 2-, and 3-year survival. Both internal and external validations of the nomogram were then completed in terms of discrimination, calibration, and clinical utility. Finally, prognostic risk stratification of GC was conducted with X-tile software.

Results

A total of 1,424 patients with GC were eligible in this study, including 1,010 in the training cohort and 414 in the validation cohort. Seven indicators were selected by LASSO to develop the nomogram, including the number of positive lymph nodes, tumor size, adjacent organ invasion, vascular invasion, the level of carbohydrate antigen 125 (CA 125), depth of invasion, and human epidermal growth factor receptor 2 (HER2) status. The nomogram demonstrated a robust predictive capacity with favorable accuracy, discrimination, and clinical utility both in the internal and external validations. Moreover, we divided the population into 3 risk groups of survival according to the cutoff points generated by X-tile, and in this way, the nomogram was further improved into a risk-stratified prognosis model.

Conclusions

We have developed a prognostic risk stratification nomogram for GC patients after radical gastrectomy with 7 available indicators that may guide clinical practice and help facilitate tailored decision-making, thus avoiding overtreatment or undertreatment and improving communication between clinicians and patients.

A Novel Nutrition-Based Nomogram to Predict Prognosis After Curative Resection of Gastric Cancer

Objective: We sought to investigate the prognostic significance of body composition and weight change during the first 6 months of adjuvant chemotherapy after R0 resection and develop novel nomograms to accurately predict relapse-free survival (RFS) and overall survival (OS).

Methods: This retrospective study included 190 patients who underwent curative radical gastrectomy for gastric cancer and received adjuvant chemotherapy. The changes in weight and body composition including skeletal muscle index (SMI), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) were analyzed for 6 months. LASSO Cox regression and multivariate Cox regression were conducted to evaluate other clinical characteristics, which were used to construct a nomogram for the prediction of 3- and 5-year RFS and OS. The constructed nomogram was subjected to 1,000 resamples bootstrap for internal validation. The Concordance index (C-index) and time-dependent receiver operating characteristic (t-ROC) curves were used to evaluate and compare the discriminative abilities of the new nomograms, non-nutritional nomograms, and pTNM stage.

Results: The median follow-up duration was 42.0 (25.2–55.1) months. Factors included in the newly-built nomogram for RFS were pT stage, pN stage, tumor site, tumor size, nerve invasion or not, surgery type, and change of L3SMI, while factors included in the nomogram for OS were pT stage, pN stage, tumor size, nerve invasion or not, surgery type, and change of L3SMI. The C-index and t-ROC indicated that our newly-built nomograms had greater potential to accurately predict prognosis than the non-nutritional nomograms and pTNM stage system. Besides, oral nutritional supplements can reduce the degree of weight and L3SMI loss.

Conclusion: Change in skeletal muscle mass during adjuvant chemotherapy can be incorporated into predictive prognostic nomograms for RFS and OS in GC patients after radical resection. Dynamic changes in body composition and weight during adjuvant chemotherapy contribute to the early detection of poor outcomes.

Application of an artificial neural network for predicting the potential chemotherapy benefit of patients with gastric cancer after radical surgery

BACKGROUND

Artificial neural network models have a strong self-learning ability and can deal with complex biological information, but there is no artificial neural network model for predicting the benefits of adjuvant chemotherapy in patients with gastric cancer.

METHODS

The clinicopathological data of patients who underwent radical resection of gastric cancer from January 2010 to September 2014 were analyzed retrospectively. Patients who underwent surgery combined with adjuvant chemotherapy were randomly divided into a training cohort (70%) and a validation cohort (30%). An artificial neural network model (potential-CT-benefit-ANN) was established, and its ability to predict the potential benefit of chemotherapy was evaluated by the C-index. The prognostic prediction and stratification ability of potential-CT-benefit-ANN and the eighth American Joint Committee on Cancer staging system were compared by receiver operating characteristic curves and Kaplan-Meier curves.

RESULTS

In both the training and validation cohort, potential-CT-benefit-ANN shows good prediction accuracy for potential adjuvant chemotherapy benefit. The receiver operating characteristic curve showed that the prediction accuracy of potential-CT-benefit-ANN was better than that of the eighth American Joint Committee on Cancer staging system in all groups. The calibration plots showed that the predicted prognosis of potential-CT-benefit-ANN was highly consistent with the actual value. The survival curves showed that potential-CT-benefit-ANN could stratify prognosis well for all groups and performed significantly better than the eighth AJCC staging system.

CONCLUSION

The potential-CT-benefit-ANN model developed in this study can accurately predict the potential benefits of adjuvant chemotherapy in patients with stage II/III gastric cancer. The benefit score based on potential-CT-benefit-ANN can predict the long-term prognosis of patients with adjuvant chemotherapy and has good prognostic stratification ability.

A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

Background:

The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC).

Methods:

GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed.

Results:

A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS.

Conclusion:

The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

[Factors affecting survival prognosis of advanced gastric cancer and establishment of a nomogram predictive model]

OBJECTIVE

To explore the factors affecting the survival of patients with advanced gastric cancer and establish a reliable predictive model of the patients' survival outcomes.

OBJECTIVE

We retrospectively collected the clinical data from patients with advanced gastric cancer treated in our department between January, 2015 and December, 2019. Univariate survival analysis was carried out using Kaplan-Meier method followed by multivariate Cox regression analysis to identify the factors associated with the survival outcomes of the patients. The R package was used to generate the survival rates, and a nomogram was established based on the results of multivariate analysis. The calibration curves and C-index were calculated to determine the predictive and discriminatory power of the model. The performance of the nomogram model for predicting the survival outcomes of the patients was evaluated using receiver- operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

OBJECTIVE

Univariate analysis showed that the number of metastatic sites, the number of treatment lines received, disease control rate (DCR) and progression-free survival (PFS) time following first-line treatment, and surgical treatment in first-line treatment were significantly correlated with the survival time of the patients (P < 0.05). Multivariate Cox regression analysis showed that surgical treatment, number of treatment lines, PFS time following first-line treatment and peritoneal metastasis, as independent prognostic factors, were significantly correlated with the patients' survival (P < 0.05). The C-index of the nomogram was 0.785 (95%CI: 0.744-0.826) for overall survival of the patients. The calibration curves showed that the actual survival rate of the patients was consistent with the predicted survival rate. The time-dependent AUC and DCA demonstrated that the nomogram had a good performance for predicting the survival outcomes of patients with advanced gastric cancer.

OBJECTIVE

Peritoneal metastasis is associated with s shorter overall survival time of patients with advanced gastric cancer, while a PFS time following first-line treatment of more than 7.0 months and third-line and posterior-line treatments are related with a longer survival time. Systematic treatment including elective surgery can improve the survival outcomes of the patients. The nomogram we established provides a reliable prognostic model for evaluating the prognosis of patients with advanced gastric cancer.

Development and External Validation of a Nomogram to Predict Recurrence-Free Survival After R0 Resection for Stage II/III Gastric Cancer: An International Multicenter Study

Background: The benefit of adjuvant chemotherapy varies widely among patients with stage II/III gastric cancer (GC), and tools predicting outcomes for this patient subset are lacking. We aimed to develop and validate a nomogram to predict recurrence-free survival (RFS) and the benefits of adjuvant chemotherapy after radical resection in patients with stage II/III GC.

Methods: Data on patients with stage II/III GC who underwent R0 resection from January 2010 to August 2014 at Fujian Medical University Union Hospital (FMUUH) (n = 1,240; training cohort) were analyzed by Cox regression to identify independent prognostic factors for RFS. A nomogram including these factors was internally and externally validated in FMUUH (n = 306) and a US cohort (n = 111), respectively.

Results: The multivariable analysis identified age, differentiation, tumor size, number of examined lymph nodes, pT stage, pN stage, and adjuvant chemotherapy as associated with RFS. A nomogram including the above 7 factors was significantly more accurate in predicting RFS compared with the 8^th AJCC-TNM staging system for patients in the training cohort. The risk of peritoneal metastasis was higher and survival after recurrence was significantly worse among patients calculated by the nomogram to be at high risk than those at low risk. The nomogram's predictive performance was confirmed in both the internal and external validation cohorts.

Conclusion: A novel nomogram is available as a web-based tool and accurately predicts long-term RFS for GC after radical resection. The tool can also be used to determine the benefit of adjuvant chemotherapy by comparing scores with and without this intervention.

Comprehensive analysis of a 14 immune-related gene pair signature to predict the prognosis and immune features of gastric cancer

BACKGROUND

As a new method for predicting tumor prognosis, the predictive effect of immune-related gene pairs (IRGPs) has been confirmed in several cancers, but there is no comprehensive analysis of the clinical significance of IRGPs in gastric cancer (GC).

METHOD

Clinical and gene expression profile data of GC patients were obtained from the GEO database. Based on the ImmPort database, differentially expressed immune-related gene (DEIRG) events were determined by a comparison of GC samples and adjacent normal samples. Cox proportional regression was used to construct an IRGP signature, and its availability was validated using three external validation datasets. In addition, we explored the association between clinical data and immune features and established a nomogram to predict outcomes in GC patients.

RESULT

A total of 88 DEIRGs were identified in GC from the training set, which formed 3828 IRGPs. Fourteen overall survival (OS)-related IRGPs were used to construct the prognostic signature. As a result, patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. In addition, the fraction of CD8+ T cells, plasma cells, CD4 memory activated T cells, and M1 macrophages was higher in the high-risk group. Expression of two immune checkpoints, CD276 and VTCN1, was significantly higher in the high-risk group as well. Based on the independent prognostic factors, a nomogram was established and showed excellent performance.

CONCLUSION

The 14 OS-related IRGP signature was associated with OS, immune cells, and immune checkpoints in GC patients, and it could provide the basis for related immunotherapy.

Development and validation of a nomogram for predicting overall survival of gastric cancer patients after D2R0 resection

OBJECTIVE

The aim of the study was to find factors associated with overall survival (OS) and establish a nomogram predicting OS of patients with gastric cancer (GC) after D2R0 resection.

METHODS

Demographic and clinicopathologic factors of patients with GC underwent D2R0 surgical resection were retrospectively collected from medical records, telephone interview or outpatient follow-up. To find factors significantly associated with OS, univariate and multivariate analyses were conducted. The concordance index (C-index) was used to measure the accuracy of the nomogram. Discrimination and calibration of the nomogram were tested using the patients in the validation set.

RESULTS

Overall, patients with 890 GC underwent D2R0 surgical resection were included. Based on the results of univariate analysis and multivariate analysis, T stage, number of metastatic local lymph nodes, lymph node positive rate, adjuvant chemotherapy and diameter of tumour were used to construct a nomogram predicting OS of patients with GC after surgical resection. In the validation cohort, the C-index was 0.73 and the nomogram performed well in predicting OS.

CONCLUSION

The nomogram was able to accurately predict OS of patients with GC underwent curative surgery and performed well in internal validation, which would also be useful for the decision-making of doctors.

Prognostic factors for laryngeal sarcoma and nomogram development for prediction: a retrospective study based on SEER database

Background

Laryngeal sarcoma is an extremely rare malignant tumor of larynx and usually reported as case reports or small series. At present, there is no research based on big data about the prognostic factors affecting laryngeal sarcoma. Our study aimed to investigate the prognostic survival factors of laryngeal sarcoma and develop a comprehensive nomogram for predicting the survival of laryngeal sarcoma.

Methods

Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database to find patients diagnosed with laryngeal sarcoma from 1998 to 2016. The data were obtained using SEER Stat 8.3.5 software, collated, and analyzed by Excel 2016 software and SPSS (v25.0). Kaplan-Meier curves were used for survival analysis. The variables obtained by univariate analysis were introduced into the Cox proportional hazard model for multivariate analysis. The risk factors affecting the prognosis of laryngeal sarcoma were obtained (P<0.05 indicated statistical significance). The independent prognostic factors of laryngeal sarcoma were integrated and used to construct a nomogram.

Results

A total of 381 patients with laryngeal sarcoma were included. The median age of diagnosis was 67 years. The proportion of patients who had received surgical treatment was 62.73%, while 22.31% of patients had received no surgery. The 1-, 3-, 5-, and 10-year survival rates were 87%, 76%, 61%, and 45%, respectively. The median survival time was 102.35 months. Univariate analysis showed that increased age, primary site, pathology, pathological grade, and surgical treatment were significantly correlated with patient survival time and were risk factors for the patients' prognosis. Race, gender, and even lymph node metastasis were not significantly correlated with patient prognosis. The risk factors obtained from the univariate analysis were incorporated into the Cox risk model for multivariate analysis, the independent risk factors for prognosis of patients were: age (HR 1.569, 95% CI: 1.358–1.813, P<0.005), pathology (HR 0.834, 95% CI: 0.734–0.948, P<0.005), pathological grade (HR 1.433, 95% CI: 1.164–1.764, P<0.001), surgical treatment (HR 0.778, 95% CI: 0.696–0.870, P<0.000), primary site was excluded (P=0.092). We included all the risk factors from the multi-factor analysis to construct a nomogram, and the C-index value was 0.73, indicating that it was well-calibrated in the medium and long term.

Conclusions

Laryngeal sarcoma is a rare malignant tumor of the larynx, which most often affects people between the ages of 50 and 79 and males. Our study shows that age, pathology, pathological grade, surgical treatment may be the risk factors for patients’ prognosis. Based on this, we constructed a nomogram model with a prediction accuracy of 73% that could help clinicians make decisions on an individual basis.

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

Background

Surgery is the only way to cure gastric adenocarcinoma (GAC), and chemotherapy is the basic adjuvant management for GAC. A significant prognostic nomogram for predicting the respective disease-specific survival (DSS) rates of GAC patients who receive surgery and chemotherapy has not been established.

Objective

We were planning to establish a survival nomogram model for GAC patients who receive surgery and chemotherapy.

Methods

We identified 5764 GAC patients who had received surgery and chemotherapy from the record of Surveillance, Epidemiology, and End Results (SEER) database. About 70% (n = 4034) of the chosen GAC patients were randomly assigned to the training set, and the rest of the included ones (n = 1729) were assigned to the external validation set. A prognostic nomogram was constructed by the training set and the predictive accuracy of it was validated by the validation set.

Results

Based on the outcome of a multivariate analysis of candidate factors, a nomogram was developed that encompassed age at diagnosis, number of regional lymph nodes examined after surgery, number of positive regional lymph nodes, sex, race, grade, derived AJCC stage, summary stage, and radiotherapy status. The C-index (Harrell’s concordance index) of the nomogram model was some larger than that of the traditional seventh AJCC staging system (0.707 vs 0.661). Calibration plots of the constructed nomogram displayed that the probability of DSS commendably accord with the survival rate. Integrated discrimination improvement (IDI) revealed obvious increase and categorical net reclassification improvement (NRI) showed visible enhancement. IDI for 3-, 5- and 10- year DSS were 0.058, 0.059 and 0.058, respectively (P > 0.05), and NRI for 3-, 5- and 10- year DSS were 0.380 (95% CI = 0.316–0.470), 0.407 (95% CI = 0.350–0.505), and 0.413 (95% CI = 0.336–0.519), respectively. Decision curve analysis (DCA) proved that the constructed nomogram was preferable to the AJCC staging system.

Conclusion

The constructed nomogram supplies more credible DSS predictions for GAC patients who receive surgery and chemotherapy in the general population. According to validation, the new nomogram will be beneficial in facilitating individualized survival predictions and useful when performing clinical decision-making for GAC patients who receive surgery and chemotherapy.

Construction of a nomogram for the prediction of prognosis in patients with resectable gastric cancer undergoing fewer than sixteen lymph node biopsies

Background

Lymph node metastases evaluation is important for assessing gastric cancer prognosis. In patients not undergoing adequate lymph node biopsy, lymph node stage migration occurs with the use of the existing staging system. This study established a prediction model to improve prognostication in patients undergoing fewer than 16 lymph nodes biopsy.

Patients and methods

In total, 3036 eligible patients from the Surveillance, Epidemiology, and End Results Program database were evaluated. They were randomized into development and validation sets in a 1:1 ratio (n=1520 and 1516, respectively). To avoid model overfitting and loss of important factors, prognostic factors related to overall survival (OS) were screened according to the Akaike information criterion. The nomogram was assessed using discrimination and consistency tests in the development and validation sets; the concordance index (C-index), calibration curves, and receiver operating characteristic (ROC) curves were also evaluated. Comparison with the 7th American Joint Committee on Cancer (AJCC) staging system was based on Kaplan–Meier curves, ROC, risk stratification, and decision curve analysis (DCA).

Results

Age, race, degree of differentiation, invasion depth, chemotherapy, radiotherapy, and lymph node ratio were independent prognostic factors in OS. C-indices of the development and validation sets were 0.759 (95% CI: 0.741–0.777) and 0.742 (95% CI: 0.713–0.771), respectively; calibration curves were approximately 45° diagonal, indicating good predictive ability of the nomogram. In contrast to the 7th AJCC staging system, the Kaplan–Meier curves and risk stratification of the nomogram had better discrimination ability, the ROC curves of the nomogram achieved more predictive accuracy, and the DCA indicated that the nomogram conferred higher net benefit.

Conclusion

Our constructed nomogram predicts the prognosis of patients with resectable gastric cancer undergoing biopsy of fewer than 16 lymph nodes more precisely and has better clinical applicability than the 7th AJCC staging system.

Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis

Background

Clinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. The aim of this study was threefold. First, to perform a systematic review to identify available clinical prediction models for patients with esophageal and/or gastric cancer. Second, to evaluate sources of bias in the included studies. Third, to investigate the predictive performance of the prediction models using meta-analysis.

Methods

MEDLINE, EMBASE, PsycINFO, CINAHL, and The Cochrane Library were searched for publications from the year 2000 onwards. Studies describing models predicting survival, adverse events and/or health-related quality of life (HRQoL) for esophageal or gastric cancer patients were included. Potential sources of bias were assessed and a meta-analysis, pooled per prediction model, was performed on the discriminative abilities (c-indices).

Results

A total of 61 studies were included (45 development and 16 validation studies), describing 47 prediction models. Most models predicted survival after a curative resection. Nearly 75% of the studies exhibited bias in at least 3 areas and model calibration was rarely reported. The meta-analysis showed that the averaged c-index of the models is fair (0.75) and ranges from 0.65 to 0.85.

Conclusion

Most available prediction models only focus on survival after a curative resection, which is only relevant to a limited patient population. Few models predicted adverse events after resection, and none focused on patient’s HRQoL, despite its relevance. Generally, the quality of reporting is poor and external model validation is limited. We conclude that there is a need for prediction models that better meet patients’ information needs, and provide information on both the benefits and harms of the various treatment options in terms of survival, adverse events and HRQoL.

Development and Validation of a Nomogram to Predict the Benefit of Adjuvant Radiotherapy for Patients with Resected Gastric Cancer

Background: The US guidelines for gastric cancer (GC) recommend adjuvant radiotherapy (ART) combined with 5-fluorouracil as a standard treatment for patients with resected locally advanced GC. However, patient selection criteria for optimizing the use of adjuvant therapies are lacking. In this study, we developed and validated a nomogram to predict the individualized overall survival (OS) benefit of ART among patients with resected ≥stage IB GC. Patients and Methods: The 2002-2006 Surveillance, Epidemiology, and End Results (SEER) data of 5,206 patients with resected GC were used as a training set for the development of a nomogram. The 2007-2008 SEER data of 1,986 patients with resected GC were used as validation data. Results: In the multivariate analysis weighted by inverse propensity score, the efficacy of ART varied by the ratio of positive to examined nodes (Pinteraction <0.01). The magnitude of this difference was included in the nomogram with associated prognosticators to predict the 3- and 5-year OS with and without ART. The nomogram showed significant prognostic superiority to the 8th TNM staging in the training set (Concordance index, 0.68 versus 0.65; P<0.01) and the validation set (Concordance index, 0.68 versus 0.64; P<0.01). Moreover, the calibration was accurate, and the actual efficacy of ART was positively correlated with the nomogram-estimated survival benefit from ART (Pinteraction <0.01 and Pinteraction =0.02 in the training set and the validation set, respectively). Conclusion: The nomogram can aid individualized clinical decision making by estimating the 3- and 5-year OS and potential benefits of ART among patients with resected GC.